Species‐specific differences in detection and occupancy probabilities help drive ability to detect trends in occupancy
Notice bibliographique
Résumé
Abstract Occupancy‐based surveys are increasingly used to monitor wildlife populations because they can be more cost‐effective than abundance surveys and because they may track multiple species, simultaneously. The design of these multi‐species occupancy surveys affects statistical power to detect trends in occupancy because individual species vary in resource selection, detection probability, and rarity. We tested for differences in the ability of a large‐scale monitoring program to detect changes in single‐species occupancy of 13 medium–large mammal species captured on n = 183 cameras systematically placed across five national parks in the Canadian Rockies (~21,000 km 2 ). We focus the interpretation of our findings on three species at risk: grizzly bear, wolverine, and caribou. We found that statistical power to monitor trends in occupancy depends not only on the established elements associated with power (sampling size, effect size, and variation in estimates), but also on species‐specific detection and occupancy probabilities. These two probabilities, however, affected power differently. For most species in our study, power is insensitive to detection probability. Increasing replicate‐specific detection probability only improved power when the cumulative detection probability was below 0.80. Therefore, efficient species monitoring must consider that power no longer improves by increasing sample size or the replicate‐specific detection probability once this threshold is reached. On the other hand, species with occupancy probabilities close to 0.5 had lower statistical power than those with higher or lower occupancy, that is, power was higher for both rare and very common species. This pattern is due to the heretofore‐underappreciated effect of the binomial variation in occupancy. The implications of these findings are species‐specific. Grizzly bears, for example, had high detection and occupancy probabilities, resulting in high power to detect a population change. Conversely, wolverines had low detection probability and the power to detect change could be improved if detection probability was increased using lure or complimentary survey techniques. Caribou, however, with both low detection and occupancy probabilities, were likely too rare on the landscape to rely on camera‐based occupancy for monitoring. Practitioners should be aware of these species‐specific trade‐offs and may need to tailor monitoring programs to prioritize particular species of conservation concern.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».